Dataset overview¶

In [1]:
# Import required libraries
import numpy as np 
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.graph_objects as go
In [2]:
# Import dataset
df = pd.read_csv("D:/CSVs/netflix_titles.csv", encoding= 'unicode_escape')
In [3]:
df.shape
Out[3]:
(8807, 12)
In [4]:
# Basic overview of dataset
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8807 entries, 0 to 8806
Data columns (total 12 columns):
 #   Column        Non-Null Count  Dtype 
---  ------        --------------  ----- 
 0   show_id       8807 non-null   object
 1   type          8807 non-null   object
 2   title         8807 non-null   object
 3   director      6173 non-null   object
 4   cast          7982 non-null   object
 5   country       7976 non-null   object
 6   date_added    8797 non-null   object
 7   release_year  8807 non-null   int64 
 8   rating        8803 non-null   object
 9   duration      8804 non-null   object
 10  listed_in     8807 non-null   object
 11  description   8807 non-null   object
dtypes: int64(1), object(11)
memory usage: 825.8+ KB
In [5]:
# Top 5 records of dataset
df.head(5)
Out[5]:
show_id type title director cast country date_added release_year rating duration listed_in description
0 s1 Movie Dick Johnson Is Dead Kirsten Johnson NaN United States September 25, 2021 2020 PG-13 90 min Documentaries As her father nears the end of his life, filmm...
1 s2 TV Show Blood & Water NaN Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban... South Africa September 24, 2021 2021 TV-MA 2 Seasons International TV Shows, TV Dramas, TV Mysteries After crossing paths at a party, a Cape Town t...
2 s3 TV Show Ganglands Julien Leclercq Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi... NaN September 24, 2021 2021 TV-MA 1 Season Crime TV Shows, International TV Shows, TV Act... To protect his family from a powerful drug lor...
3 s4 TV Show Jailbirds New Orleans NaN NaN NaN September 24, 2021 2021 TV-MA 1 Season Docuseries, Reality TV Feuds, flirtations and toilet talk go down amo...
4 s5 TV Show Kota Factory NaN Mayur More, Jitendra Kumar, Ranjan Raj, Alam K... India September 24, 2021 2021 TV-MA 2 Seasons International TV Shows, Romantic TV Shows, TV ... In a city of coaching centers known to train I...
In [6]:
# Renaming columns of dataset (according to your habbit/comfort)
# Makes it easy while ploting
df.rename(columns={'release_year':'ReleaseYear','show_id':'ShowId','type':'Type','title':'Title','director':'Director','csat':'Cast','country':'country','date_added':'DateAdded','rating':'Ratings','duration':'Duration','listed_in':'ListedIn','description':'Description'},inplace=True)
In [7]:
df.head(5)
Out[7]:
ShowId Type Title Director cast country DateAdded ReleaseYear Ratings Duration ListedIn Description
0 s1 Movie Dick Johnson Is Dead Kirsten Johnson NaN United States September 25, 2021 2020 PG-13 90 min Documentaries As her father nears the end of his life, filmm...
1 s2 TV Show Blood & Water NaN Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban... South Africa September 24, 2021 2021 TV-MA 2 Seasons International TV Shows, TV Dramas, TV Mysteries After crossing paths at a party, a Cape Town t...
2 s3 TV Show Ganglands Julien Leclercq Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi... NaN September 24, 2021 2021 TV-MA 1 Season Crime TV Shows, International TV Shows, TV Act... To protect his family from a powerful drug lor...
3 s4 TV Show Jailbirds New Orleans NaN NaN NaN September 24, 2021 2021 TV-MA 1 Season Docuseries, Reality TV Feuds, flirtations and toilet talk go down amo...
4 s5 TV Show Kota Factory NaN Mayur More, Jitendra Kumar, Ranjan Raj, Alam K... India September 24, 2021 2021 TV-MA 2 Seasons International TV Shows, Romantic TV Shows, TV ... In a city of coaching centers known to train I...
In [8]:
# Stats about the dataset
df.describe()
Out[8]:
ReleaseYear
count 8807.000000
mean 2014.180198
std 8.819312
min 1925.000000
25% 2013.000000
50% 2017.000000
75% 2019.000000
max 2021.000000
In [9]:
# Display column names in dataset
df.columns
Out[9]:
Index(['ShowId', 'Type', 'Title', 'Director', 'cast', 'country', 'DateAdded',
       'ReleaseYear', 'Ratings', 'Duration', 'ListedIn', 'Description'],
      dtype='object')

Data cleaning¶

In [10]:
df1=df[['Type','Title','Director','cast','country','ReleaseYear','Ratings','Duration','ListedIn']]
df1
Out[10]:
Type Title Director cast country ReleaseYear Ratings Duration ListedIn
0 Movie Dick Johnson Is Dead Kirsten Johnson NaN United States 2020 PG-13 90 min Documentaries
1 TV Show Blood & Water NaN Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban... South Africa 2021 TV-MA 2 Seasons International TV Shows, TV Dramas, TV Mysteries
2 TV Show Ganglands Julien Leclercq Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi... NaN 2021 TV-MA 1 Season Crime TV Shows, International TV Shows, TV Act...
3 TV Show Jailbirds New Orleans NaN NaN NaN 2021 TV-MA 1 Season Docuseries, Reality TV
4 TV Show Kota Factory NaN Mayur More, Jitendra Kumar, Ranjan Raj, Alam K... India 2021 TV-MA 2 Seasons International TV Shows, Romantic TV Shows, TV ...
... ... ... ... ... ... ... ... ... ...
8802 Movie Zodiac David Fincher Mark Ruffalo, Jake Gyllenhaal, Robert Downey J... United States 2007 R 158 min Cult Movies, Dramas, Thrillers
8803 TV Show Zombie Dumb NaN NaN NaN 2018 TV-Y7 2 Seasons Kids' TV, Korean TV Shows, TV Comedies
8804 Movie Zombieland Ruben Fleischer Jesse Eisenberg, Woody Harrelson, Emma Stone, ... United States 2009 R 88 min Comedies, Horror Movies
8805 Movie Zoom Peter Hewitt Tim Allen, Courteney Cox, Chevy Chase, Kate Ma... United States 2006 PG 88 min Children & Family Movies, Comedies
8806 Movie Zubaan Mozez Singh Vicky Kaushal, Sarah-Jane Dias, Raaghav Chanan... India 2015 TV-14 111 min Dramas, International Movies, Music & Musicals

8807 rows × 9 columns

In [11]:
# Checking null values
pd.isnull(df1)
Out[11]:
Type Title Director cast country ReleaseYear Ratings Duration ListedIn
0 False False False True False False False False False
1 False False True False False False False False False
2 False False False False True False False False False
3 False False True True True False False False False
4 False False True False False False False False False
... ... ... ... ... ... ... ... ... ...
8802 False False False False False False False False False
8803 False False True True True False False False False
8804 False False False False False False False False False
8805 False False False False False False False False False
8806 False False False False False False False False False

8807 rows × 9 columns

In [12]:
# Sum of null values 
pd.isnull(df1).sum()
Out[12]:
Type              0
Title             0
Director       2634
cast            825
country         831
ReleaseYear       0
Ratings           4
Duration          3
ListedIn          0
dtype: int64
In [13]:
df1.fillna({'Director':'Unavailable'},inplace=True)

C:\Users\hp\AppData\Local\Temp\ipykernel_19192\1365858580.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df1.fillna({'Director':'Unavailable'},inplace=True)
In [14]:
# Filling null values with sutaible values
df1.fillna({'Director':'Not available'},inplace=True)

C:\Users\hp\AppData\Local\Temp\ipykernel_19192\4170769459.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df1.fillna({'Director':'Not available'},inplace=True)
In [15]:
df1.fillna({'cast':'Others'},inplace=True)

C:\Users\hp\AppData\Local\Temp\ipykernel_19192\3999689768.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df1.fillna({'cast':'Others'},inplace=True)
In [16]:
df1.fillna({'country':'Others'},inplace=True)

C:\Users\hp\AppData\Local\Temp\ipykernel_19192\3410488415.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df1.fillna({'country':'Others'},inplace=True)
In [17]:
df1.fillna({'DateAdded':'Not availabe'},inplace=True)
In [18]:
df1.fillna({'Ratings':'Not availabe'},inplace=True)

C:\Users\hp\AppData\Local\Temp\ipykernel_19192\3716011444.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df1.fillna({'Ratings':'Not availabe'},inplace=True)
In [19]:
df1.fillna({'duration':'Not availabe'},inplace=True)
In [20]:
#dropna can also be used here but dropna drops columns having missing values instead of missing values
#Droping null values
#df.dropna(inplace=True)
In [21]:
# To check weather null values are removed or not
pd.isnull(df1).sum()
Out[21]:
Type           0
Title          0
Director       0
cast           0
country        0
ReleaseYear    0
Ratings        0
Duration       3
ListedIn       0
dtype: int64
In [22]:
df1.shape
Out[22]:
(8807, 9)
In [23]:
df1.to_excel('df1.xlsx', index=False)

Analysis & Visualization¶

In [24]:
# Get type of shows from dataset
df1.Type
Out[24]:
0         Movie
1       TV Show
2       TV Show
3       TV Show
4       TV Show
         ...   
8802      Movie
8803    TV Show
8804      Movie
8805      Movie
8806      Movie
Name: Type, Length: 8807, dtype: object
In [25]:
# Total count of types of shows
df1.Type.value_counts()
Out[25]:
Movie      6131
TV Show    2676
Name: Type, dtype: int64
In [26]:
# Countplot of types of shows
cp = sns.countplot(x='Type',data=df1)
# Adding labels to bars in plot
for bars in cp.containers:
    cp.bar_label(bars)
sns.set(rc={'figure.figsize':(10,5)})
# Adding title & display
plt.title('Type of shows')
plt.show()
In [27]:
# Count of ratings
df1.Ratings.value_counts().head(10)
Out[27]:
TV-MA    3207
TV-14    2160
TV-PG     863
R         799
PG-13     490
TV-Y7     334
TV-Y      307
PG        287
TV-G      220
NR         80
Name: Ratings, dtype: int64
In [28]:
# pie chart of ratings given by top 10 rating systems
pc=df1['Ratings'].value_counts().head(10)[:10] # Index
circle=go.Figure(data=[go.Pie(labels= list(pc.index),values= list(pc.values))]) # takes parameters as labels & values
circle.update_layout(title_text="Overall ratings given by rating systems ",) # for title
circle.show()
In [39]:
# Countplot of ratings given by rating systems (type-wise)
cp1=sns.countplot(x='Ratings',data=df1,hue='Type')
# Adding labels to bars
for bars in cp1.containers:
    cp1.bar_label(bars)
# Size of bars
sns.set(rc={'figure.figsize':(10,5)})
plt.title('type-wise ratings by rating systems')
plt.show()
In [30]:
# Count of country column(top 6 countries)
df1.country.value_counts().head(5+1)
Out[30]:
United States     2818
India              972
Others             831
United Kingdom     419
Japan              245
South Korea        199
Name: country, dtype: int64
In [31]:
# Countplot of country-wise types of shows on netflix
# hue is used to split categorical values
cp2=sns.countplot(x='country',order=df['country'].value_counts().index[0:6],data=df1)
for bars in cp2.containers:
    cp2.bar_label(bars)
sns.set(rc={'figure.figsize':(20,5)})
plt.title('Country-wise shows on Netlfix')
plt.show()
In [32]:
# Adding hue split is above plot
# hue is used to split categorical values
cp2=sns.countplot(x='country',order=df['country'].value_counts().index[0:6],data=df1,hue='Type')
for bars in cp2.containers:
    cp2.bar_label(bars)
sns.set(rc={'figure.figsize':(20,5)})
plt.title('Country-wise shows on Netlfix')
plt.show()
In [42]:
# Countplot of types of show released every year 
cp3=sns.countplot(data=df1,x='ReleaseYear',hue='Type')
sns.set(rc={'figure.figsize':(15,5)})
for bars in cp3.containers:
    cp3.bar_label(bars)
plt.title('Types of shows released every year')
plt.show
Out[42]:
<function matplotlib.pyplot.show(close=None, block=None)>
In [34]:
# Pie chart of country-wise shows production(in percent)
pc1=df1['country'].value_counts()[:10]
# go.figure creates new figure that will hold visualization
# go.pie is used to create pie chart that take values and labels parameters
fig1=go.Figure(data=[go.Pie(labels= list(pc1.index),values= list(pc1.values),hole=0.3)])
# update_layout is used to set title
fig1.update_layout(title_text="Top 10 country-wise production ")
fig1.show()
In [35]:
watched = df1.groupby(['Type'],as_index=False)['ReleaseYear'].sum().sort_values(by='ReleaseYear', ascending=False)
watched
Out[35]:
Type ReleaseYear
0 Movie 12342448
1 TV Show 5396437
In [36]:
# Barplot of types and releaseyear
watched1 = sns.barplot(x='Type',y='ReleaseYear',data=watched)
for bars in watched1.containers:
    watched1.bar_label(bars)
sns.set(rc={'figure.figsize':(5,5)})
    
# the number is 1.0435 multiplied by 10 to the power of 7.
# 1.0435e+07 is a scientific notation or exponential notation that represents a number
In [37]:
# grouping based on types, release year and count of titles
grouped = (df.groupby(['Type','ReleaseYear'])['Title'].size()).reset_index()
grouped
Out[37]:
Type ReleaseYear Title
0 Movie 1942 2
1 Movie 1943 3
2 Movie 1944 3
3 Movie 1945 3
4 Movie 1946 1
... ... ... ...
114 TV Show 2017 265
115 TV Show 2018 380
116 TV Show 2019 397
117 TV Show 2020 436
118 TV Show 2021 315

119 rows × 3 columns

In [38]:
# Lineplot to compare types of shows year-wise
lp = sns.set(rc={'figure.figsize':(15,5)})
sns.lineplot(data = grouped, x = 'ReleaseYear', y = 'Title', hue = 'Type')
plt.title('Year-wise total content')
plt.show()

Summary (According to your questions or requirments)¶

Netflix TV shows and movies dataset is a collection of information about the titles available on Netflix as of 2021.¶

1) Count of titles has increased significantly over the years. 2) Specifically, two type of titles are available on Netflix:'Movies' & 'Tv-shows' 3) Top most rating system is 'TV-MA' which has rated over 36.7% show (3207). 4) United States is the country with most release on Netflix with record of 2818 total shows followed by India, United Kingdom, Japan & South Korea. 5) United States has the largest production count with 46.6% 6) If you see the comparsiosn chart theres significant growth graph of TV-Shows during the year 2019-2020, covid pandamic is one of the biggest reasons for this change.